Autonomous driving technology requires continuous sensor data processing of a situational environment of the self-driving vehicle (SDV). One limiting factor in the rollout of SDVs on public roads and highway is uncertainty with respect to the overall safety of such vehicles in normal driving situations as well as any contingency scenario. Traditional safety concepts may be necessary but insufficient for the SDV domain. For instance, SDV systems are extremely complex, having a very large and intricate code base that may be impractical to rigorously validate.
Additionally, the range of possible situations, interactions, and scenarios is virtually infinite, so safety guarantees for every conceivable scenario may not be possible. Thus, a rigorous design-based safety concept, which may often be preferred for typical software projects, may be impractical for extremely complex systems running in arbitrary, random, uncontrolled, and changing environments. Yet, in order to provide the necessary safeguards for SDV operation on public roads and highways, SDV manufacturers and operators must have a means for measuring and understanding just how safe an SDV is in its operation.